binex 0.3.0

Debuggable runtime for AI agent pipelines

Binex

Debuggable runtime for AI agent pipelines
Orchestrate multi-agent workflows. Trace every step. Replay and diff runs.

Installation · Quickstart · Demo · Documentation · Issues

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What is Binex?

Binex is a debuggable runtime for AI agent workflows.

It executes DAG-based pipelines of agents (LLM, local, remote A2A, or human), tracks artifacts between steps, and allows replaying and inspecting runs.

Key features:

• DAG-based execution — define agent pipelines as readable YAML, not tangled code
• Artifact lineage — every input and output tracked across the entire pipeline
• Replayable workflows — re-run with agent swaps, compare results
• Full tracing — every node call, every artifact, every millisecond recorded
• Post-mortem debugging — inspect any run after the fact with rich reports
• Run diffing — compare two executions side-by-side to spot regressions
• Human-in-the-loop — approval gates and free-text input with conditional branching
• Budget & cost tracking — per-node cost records, budget enforcement (stop/warn), CLI cost inspection
• Framework adapters — plug in LangChain, CrewAI, or AutoGen agents with a single URI
• Plugin system — extend Binex with custom adapter plugins via entry points
• CLI-first DX — everything accessible from the terminal

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Installation

Install from PyPI:

pip install binex

With framework adapters:

pip install binex[langchain]    # LangChain Runnables
pip install binex[crewai]       # CrewAI Crews
pip install binex[autogen]      # AutoGen Teams
pip install binex[langchain,crewai,autogen]  # all three

For rich colored output:

pip install binex[rich]

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Quickstart

Run the built-in demo (no config needed):

binex hello

Create a workflow file workflow.yaml:

name: hello
nodes:
  greet:
    agent: "local://echo"
    inputs:
      msg: "hello world"
    outputs: [response]

  respond:
    agent: "local://echo"
    inputs:
      greeting: "${greet.response}"
    depends_on: [greet]

Run it:

binex run workflow.yaml

Inspect the run:

binex debug latest
binex trace latest

See it in action

$ binex hello

Running built-in hello-world workflow...

  [1/2] greeter ...
  [greeter] -> result:
Hello from Binex!

  [2/2] responder ...
  [responder] -> result:
{"greeter": "Hello from Binex!"}

Run completed (2/2 nodes)
Run ID: run_d71c9a50

Next steps:
  binex debug run_d71c9a50    — inspect the run
  binex init                  — create your own project
  binex run examples/simple.yaml — try a workflow file

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Demo

A multi-provider research pipeline: Ollama runs locally for planning and summarization, OpenRouter calls cloud models for parallel research — all in one YAML file.

Requirements to run this demo

• Ollama installed and running locally
• Model pulled: ollama pull gemma3:4b
• Free OpenRouter API key (set OPENROUTER_API_KEY in .env)
• Binex installed: pip install binex

# examples/multi-provider-demo.yaml
name: multi-provider-research

nodes:
  user_input:
    agent: "human://input"

  planner:
    agent: "llm://ollama/gemma3:4b"
    system_prompt: "Create a structured research plan with 3 subtopics..."
    inputs: { topic: "${user_input.result}" }
    depends_on: [user_input]

  researcher1:
    agent: "llm://openrouter/nvidia/nemotron-3-super-120b-a12b:free"
    inputs: { plan: "${planner.result}" }
    depends_on: [planner]

  researcher2:
    agent: "llm://openrouter/nvidia/nemotron-3-super-120b-a12b:free"
    inputs: { plan: "${planner.result}" }
    depends_on: [planner]

  summarizer:
    agent: "llm://ollama/gemma3:4b"
    inputs: { research1: "${researcher1.result}", research2: "${researcher2.result}" }
    depends_on: [researcher1, researcher2]

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Start Wizard

Create a full research pipeline from a template in seconds:

Or build your own workflow node-by-node with the custom constructor:

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Explore Dashboard

Interactive run inspector — trace, graph, cost, artifacts, node detail, debug — all in one place:

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How It Works

Define a workflow in YAML. Binex builds a DAG, schedules nodes respecting dependencies, dispatches each to the right agent adapter, and records everything.

name: research-pipeline
description: "Fan-out research with human approval"

nodes:
  planner:
    agent: "llm://openai/gpt-4"
    system_prompt: "Break this topic into 3 research questions"
    inputs:
      topic: "${user.topic}"
    outputs: [questions]

  researcher_1:
    agent: "llm://anthropic/claude-sonnet-4-20250514"
    inputs: { question: "${planner.questions}" }
    outputs: [findings]
    depends_on: [planner]

  researcher_2:
    agent: "a2a://localhost:8001"
    inputs: { question: "${planner.questions}" }
    outputs: [findings]
    depends_on: [planner]

  reviewer:
    agent: "human://approve"
    inputs:
      draft: "${researcher_1.findings}"
    outputs: [decision]
    depends_on: [researcher_1, researcher_2]

  summarizer:
    agent: "llm://openai/gpt-4"
    inputs:
      research: "${researcher_1.findings}"
    outputs: [summary]
    depends_on: [reviewer]
    when: "${reviewer.decision} == approved"

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Architecture

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Features

Agent Adapters

Prefix	Adapter	Description
local://	LocalPythonAdapter	In-process Python callable
llm://	LLMAdapter	LLM completion via LiteLLM (40+ providers)
a2a://	A2AAgentAdapter	Remote agent via A2A protocol
human://input	HumanInputAdapter	Terminal prompt for free-text input
human://approve	HumanApprovalAdapter	Approval gate with conditional branching
langchain://	LangChainAdapter	LangChain Runnable via plugin (requires binex[langchain])
crewai://	CrewAIAdapter	CrewAI Crew via plugin (requires binex[crewai])
autogen://	AutoGenAdapter	AutoGen Team via plugin (requires binex[autogen])

CLI Commands

Command	Description
binex run <workflow.yaml>	Execute a workflow
binex debug <run-id|latest>	Post-mortem inspection (--json, --errors, --node, --rich)
binex trace <run-id>	Execution timeline, node details, or DAG graph
binex replay <run-id>	Re-run with optional agent swaps
binex diff <run1> <run2>	Compare two runs side-by-side
binex artifacts list <run-id>	List artifacts with lineage tracking
binex validate <workflow.yaml>	Validate YAML before execution
binex scaffold workflow "A -> B"	Generate workflow from DSL shorthand
binex init	Interactive project setup
binex dev up	Start Docker dev stack
binex doctor	Check system health
binex cost show <run-id>	Cost breakdown per node (--json)
binex cost history <run-id>	Chronological cost events (--json)
binex explore	Interactive browser for runs and artifacts
binex plugins list	Show built-in adapters and installed plugins (--json)
binex plugins check <workflow>	Validate all agent URIs are resolvable
binex hello	Zero-config demo

LLM Providers

Out-of-the-box support for 9 providers via LiteLLM:

OpenAI · Anthropic · Google Gemini · Ollama · OpenRouter · Groq · Mistral · DeepSeek · Together AI

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Examples

Example workflows are available in the examples/ directory:

Example	What it demonstrates
simple.yaml	Minimal two-node pipeline
diamond.yaml	Diamond dependency pattern
fan-out-fan-in.yaml	Parallel execution with aggregation
human-in-the-loop.yaml	Approval gates and conditional branching
multi-provider-demo.yaml	Multiple LLM providers in one workflow
a2a-multi-agent.yaml	Remote agents via A2A protocol
langchain-summarizer.yaml	LangChain Runnable in a pipeline
crewai-research-crew.yaml	CrewAI Crew as a workflow node
autogen-coding-team.yaml	AutoGen Team for code generation
mixed-framework-pipeline.yaml	LLM + LangChain + CrewAI + AutoGen combined
conditional-routing.yaml	Branch based on node output
map-reduce.yaml	MapReduce-style aggregation

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Documentation

Full documentation is available at alexli18.github.io/binex:

• Quickstart — install and run your first workflow
• Concepts — agents, workflows, artifacts, execution model
• CLI Reference — every command with options and examples
• Architecture — runtime internals and design decisions
• Workflow Format — complete YAML schema reference

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Project Structure

src/binex/
├── adapters/        # Agent backends (local, LLM, A2A, human, LangChain, CrewAI, AutoGen)
├── agents/          # Built-in agent implementations
├── cli/             # Click CLI commands
├── graph/           # DAG construction + topological scheduling
├── models/          # Pydantic v2 domain models
├── registry/        # FastAPI agent registry service
├── runtime/         # Orchestrator, dispatcher, lifecycle
├── plugins/         # Plugin registry for custom adapter discovery
├── stores/          # SQLite execution + filesystem artifact persistence
├── trace/           # Debug reports, lineage, timeline, diffing
├── workflow_spec/   # YAML loader + validator + variable resolution
└── tools.py         # Tool calling support (@tool decorator)

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Roadmap

See ROADMAP.md for upcoming features.

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Contributing

Contributions are welcome! See CONTRIBUTING.md for development setup and guidelines.

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License

Distributed under the MIT License. See LICENSE for more information.

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_{Built with focus on debuggability, because AI agents shouldn't be black boxes.}